机器学习在材料科学中的应用进展

Applications of machine learning in material science

材料作为直接推动科学发展和社会进步的动力,其结构和性能研究一直是材料科学领域的研究热点。然而,传统实验研究和计算模拟方法有时难以满足材料科学研发需求。机器学习因其泛化能力强、计算速度快等优势已然成为材料性能预测和新材料筛选方面最具潜力的研究方法之一。将系统阐述机器学习计算方法的基本流程和常用算法,详细介绍该方法在材料性能预测和新材料设计方面的应用,并对其在材料科学领域面临的挑战和应用前景进行总结和展望。

As one of the driving forces to promote scientific development and social progress,the structure and properties of materials have always been the focus of scientific research.The traditional experimental research and computational simulation methods have been difficult to meet the growing needs of material research and development.With the rise of artificial intelligence,a method based on machine learning computing to predict material properties and find new materials has become the most potential and efficient research method.This work systematically introduces the basic process and common algorithms of machine learning calculation method,and further briefly introduces its practical application in material property prediction and new material discovery.Finally,the application status of machine computing learning in material property prediction and new material discovery is summarized,and its broad application prospect in the field of material science in the future is prospected.